1. Field of Invention
The present patent document is directed towards systems and methods for object detection. More particularly, the present patent document is directed towards systems and methods for generating and using object detection models that include contextual information.
2. Description of the Related Art
Object detection from images can be important to many applications, such as surveillance, robotics, manufacturing, security, medicine, and automotive safety—just to name a few areas of application. However, object detection is among the most challenging vision tasks due in part to the great variety in appearance and shape of objects, highly cluttered environments, and often low resolution and low quality image sources.
Predominant approaches for object detection usually involve scanning an input image with sliding windows to identify the locations of image patches that may contain the object of interest. To determine whether a local window includes the object of interest, both generative and discriminative approaches have been developed. The generative approaches typically infer the posterior probability for the object class using discrete or continuous shape models, or combining shape and texture models. The discriminative approaches extract image features in the local window and construct classifiers for detection. For this purpose, various features have been proposed, such as Haar wavelet features, gradient-based features, shape-based features, combination of multiple features, automatically mined features, or pose-invariant features. The local features are then used to identify the object of interest in a classification process by algorithms such as AdaBoost or support vector machine (SVM).
It must be noted, however, that these methods generally only utilize information inside an image patch region. That is, these detection methods seek to detect the object without taking into account the context. Furthermore, these prior approaches do not adequately consider that objects at different sizes have qualitatively very different appearances.
Accordingly, systems and methods are needed that can address the challenges presented when trying to detect an object or item in an image.